INTRODUCTION Ferritin is the best biomarker for assessing iron deficiency, but ferritin concentrations increase with inflammation. Several adjustment methods have been proposed to account for inflammation’s effect on iron biomarker interpretation. The most recent and highly recommended method uses linear regression models, but more research is needed on other models that may better define iron status in children, particularly when distributions are heterogenous and in contexts where the effect of inflammation on ferritin is not linear.
OBJECTIVES Assess the utility and relevance of quadratic regression models and quantile quadratic regression models in adjusting ferritin concentration in the presence of inflammation.
METHODS We used data from children aged under five years, taken from Cuba’s national anemia and iron deficiency survey, which was carried out from 2015–2018 by the National Hygiene, Epidemiology and Microbiology Institute. We included data from 1375 children aged 6 to 59 months and collected ferritin concentrations and two biomarkers for inflammation: C-reactive protein and α-1 acid glycoprotein. Quadratic regression and quantile regression models were used to adjust for changes in ferritin concentration in the presence of inflammation.
RESULTS Unadjusted iron deficiency prevalence was 23% (316/1375). Inflammation-adjusted ferritin values increased iron-deficiency prevalence by 2.6–4.5 percentage points when quadratic regression correction model was used, and by 2.8–6.2 when quantile regression was used. The increase when using the quantile regression correction model was more pronounced and statistically significant when both inflammation biomarkers were considered, but adjusted prevalence was similar between the two correction methods when only one biomarker was analyzed.
CONCLUSIONS The use of quadratic regression and quantile quadratic regression models is a complementary strategy in adjusting ferritin for inflammation, and is preferable to standard regression analysis when the linear model’s basic assumptions are not met, or when it can be assumed that ferritin–inflammation relationships within a subpopulation may deviate from average trends.
KEYWORDS Alpha-1-acid glycoprotein, C-reactive protein, anemia, iron deficiency, ferritin, acute phase protein, Cuba
INTRODUCTION Anemia is a public health problem worldwide and is most prevalent in preschool children, for whom it is the most frequent cause of nutritional deficits. In turn, iron deficiency is the main cause of anemia, affecting 43% of children globally. Previous studies in Cuba show rates of iron deficiency in preschool children between 38.6% and 57.6%, higher in infants (71.2% to 81.1%). WHO recommends using serum ferritin as an indicator of iron deficiency accompanied by acute (C-reactive protein) and chronic (α1-acid glycoprotein) inflammation biomarkers.
OBJECTIVE Assess how inflammation affects measuring and reporting of iron-deficiency anemia rates in Cuban preschool children.
METHODS Data were obtained from serum samples contained in the National Anemia and Iron Deficiency Survey, and included presumably healthy preschool Cuban children (aged 6–59 months). Serum samples were collected from 1375 children from randomly selected provinces in 4 regions of the country from 2014 through 2018. We examined the association between ferritin and two inflammatory biomarkers: C-reactive protein and α1-acid glycoprotein. Individual inflammation-adjusted ferritin concentrations were calculated using four approaches: 1) a higher ferritin cut-off point (<30 g/L); 2) exclusion of subjects showing inflammation (C-reactive protein >5 mg/L or α1-acid glycoprotein >1 g/L); 3) mathematical correction factor based on C-reactive protein or α1-acid glycoprotein; and 4) correction by regression with the method proposed by the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia Group. We estimated confidence intervals of differences between unadjusted prevalence and prevalence adjusted for inflammation by each method.
RESULTS The proportion of children with inflammation according to C-reactive protein concentrations >5 mg/L was lower (11.1%, 153/1375) than the proportion measured according to the concentrations of α1-acid glycoprotein, at >1 g/L (30.8%, 424/1375). The percentage of children with high concentrations of at least one of the aforementioned biomarkers was 32.7% (450/1375). Thus, each correction method increased the observed prevalence of iron deficiency compared to unadjusted estimates (23%, 316/1375). This increase was more pronounced when using the internal regression correction method (based only on C-reactive protein) or the method based on a higher cut-off point. Adjustment using all four methods changed estimated iron deficiency prevalence, increasing it from 0.1% to 8.8%, compared to unadjusted values.
CONCLUSION One-third of preschool children had biomarkers indicating elevated inflammation levels. Without adjusting for inflammation, iron deficiency prevalence was underestimated. The significant disparity between unadjusted and inflammation-adjusted ferritin when using some approaches highlights the importance of selecting the right approach for accurate, corrected measurement. The internal regression correction approach is appropriate for epidemiological studies because it takes into account inflammation severity. However, other models should be explored that account for inflammation and also provide better adjusted ferritin concentrations.
KEYWORDS Anemia, iron deficiency; child, preschool; inflammation; Cuba
